Forum Discussion
How are people estimating LLM API costs before putting an AI application into production?
It's relatively easy to estimate the number of API requests, but I'm finding the token side more interesting because input and output usage can vary considerably between requests. Things get even more complicated when comparing different models or using long prompts and conversation history.
Do you normally build a spreadsheet for this, use the provider's pricing information directly, or use a dedicated calculator?
I'm especially interested in approaches that estimate monthly cost from expected requests, average input tokens, average output tokens, and the model being used.
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I usually start with a spreadsheet that estimates average input and output tokens per request, then multiply those figures by the expected monthly request volume. I plug in each model’s current pricing to compare costs. I also account for conversation history, retries, and peak usage, since they can significantly affect the final bill. For example, if an AI application uses the Nonprofit Check Plus API for verification, I’d include those API calls in the overall usage estimate. Running a small test with real prompts before launch helps validate the estimates.
